Q-Learning Assisted Efficient Collision Detection and Avoidance Framework for Autonomous Vehicles in ITS

In the evolving landscape of Intelligent Transportation Systems (ITS), autonomous vehicles (AVs) are crucial in enhancing road safety and traffic efficiency. This paper presents a novel framework for autonomous driving systems that integrates a reinforcement learning technique known as Q-Learning to enhance decision-making in dynamic environments. The proposed framework is divided into several layers, including the Autonomous Layer, Data Filtering Layer, and Q-Learning Layer, each contributing to a robust system that can effectively manage real-time data and make adaptive driving decisions. The Autonomous Layer leverages sensors like LIDAR, radar, and heat sensors to gather environmental data, which the Data Collection Layer processes to inform the Q-learning model. The Q-learning model is then used to optimize driving policies by considering various factors such as collision avoidance, obstacle detection, and relative distance measurement. The system’s performance was evaluated through simulations, demonstrating its ability to improve safety and efficiency in autonomous driving scenarios. The results indicate that integrating reinforcement learning significantly enhances the system’s adaptability and reliability, offering a promising approach for future autonomous vehicle technologies.